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Advances in independent component analysis and learning machines /
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have dev...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, UK :
Academic Press,
2015.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Advances in independent component analysis and learning machines / |c edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen. |
| 264 | 1 | |a London, UK : |b Academic Press, |c 2015. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ScienceDirect, viewed May 27, 2015). | |
| 505 | 0 | |a Front Cover; Advances in Independent Component Analysis and Learning Machines; Copyright; Contents; Preface; About the Editors; List of Contributors; Introduction; A Student and a Co-Worker; Prof. Simon Haykin; Prof. Jos�e Pr�incipe; Prof. T�ulay Adali; Prof. Lu�is Borges de Almeida; Prof. Christian Jutten; Prof. Mark Plumbley; Prof. Klaus-Robert M�uller and Dr. Andreas Ziehe; Chapter abstracts; Chapter 1; The initial convergence rate of the FastICA algorithm: The ``One-Third Rule''; Scott C. Douglas; Chapter 2; Improved variants of the FastICA algorithm; Zbynvek Koldovsk�y and Petr Tichavsk�y | |
| 505 | 8 | |a Chapter 3A unified probabilistic model for independent and principal component analysis; Aapo Hyv�arinen; Chapter 4; Riemannian optimization in complex-valued ICA; Visa Koivunen and Traian Abrudan; Chapter 5; Nonadditive optimization; Zhirong Yang and Irwin King; Chapter 6; Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning; Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng and Lei Xu; Chapter 7; Unsupervised deep learning: A short review; Juha Karhunen, Tapani Raiko and KyungHyun Cho; Chapter 8; From neural PCA to deep unsupervised learning; Harri Valpola; Chapter 9. | |
| 505 | 8 | |a Two decades of local binary patterns: A surveyMatti Pietik�ainen and Guoying Zhao; Chapter 10; Subspace approach in spectral color science; Jussi Parkkinen, Hannu Laamanen and Markku Hauta-Kasari; Chapter 11; From pattern recognition methods to machine vision applications; Heikki K�alvi�ainen; Chapter 12; Advances in visual concept detection: Ten years of TRECVID; Ville Viitaniemi, Mats Sj�oberg, Markus Koskela, Satoru Ishikawa and Jorma Laaksonen; Chapter 13; On the applicability of latent variable modeling to research system data; Ella Bingham and Heikki Mannila; Part I: Methods. | |
| 505 | 8 | |a Chapter 1: The initial convergence rate of the FastICA algorithm: The ``One-Third Rule''1.1 Introduction; 1.2 Statistical analysis of the FastICA algorithm; 1.3 Stationary point analysis of the FastICA algorithm; 1.4 Initial convergence of the FastICA algorithm for two-source mixtures; 1.4.1 Overview of results; 1.4.2 Preliminaries; 1.4.3 Equal-kurtosis sources case; 1.4.3.1 A bound on the average ICI; 1.4.3.2 The probability density function of the ICI; 1.4.3.3 The average value of the ICI; 1.4.4 Arbitrary-kurtosis sources case. | |
| 505 | 8 | |a 1.5 Initial convergence of the FastICA algorithm for three or more source mixtures1.5.1 Overview of results; 1.5.2 Preliminaries; 1.5.3 Three-source case; 1.5.4 Four-source case; 1.5.5 General m-source case; 1.5.6 Equal-kurtosis m-source case using order statistics; 1.6 Numerical evaluations; 1.7 Conclusion; Appendix; Proof of Theorem 1; Proof of Theorems 2 and 3; Proof of Theorem 4; Proofs of Theorem 5 and Associated Corollaries; Proof of Theorem 6; Proof of Theorem 7; Proof of Theorem 8; Proof of Theorem 9; Proof of Theorem 10; Proof of Theorem 11; Proof of Theorem 12; Acknowledgments. | |
| 520 | |a In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t. | ||
| 504 | |a Includes bibliographical references at the end of each chapters and index. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Pattern Recognition, Automated |0 (DNLM)D010363 | |
| 650 | 2 | |a Machine Learning |0 (DNLM)D000069550 | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) |0 (CaQQLa)201-0028094 | |
| 650 | 6 | |a Traitement d'images |x Techniques num�eriques. |0 (CaQQLa)201-0118646 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a digital imaging. |2 aat |0 (CStmoGRI)aat300237903 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Image processing |x Digital techniques |2 fast |0 (OCoLC)fst00967508 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Pattern recognition systems |2 fast |0 (OCoLC)fst01055266 | |
| 655 | 7 | |a Festschriften |2 fast |0 (OCoLC)fst01941036 | |
| 655 | 7 | |a Festschriften. |2 lcgft | |
| 700 | 1 | |a Bingham, Ella, |e editor. | |
| 700 | 1 | |a Kaski, Samuel, |e editor. | |
| 700 | 1 | |a Laaksonen, Jorma, |e editor. | |
| 700 | 1 | |a Lampinen, Jouko, |e editor. | |
| 700 | 1 | |a Oja, Erkki, |e honouree. | |
| 776 | 0 | 8 | |i Print version: |a Bingham, Ella. |t Advances in Independent Component Analysis and Learning Machines. |d Burlington : Elsevier Science, �2015 |z 9780128028063 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128028063 |z Texto completo |